2016
Conferences
Adrian-Gabriel Chifu; Sébastien Fournier
20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, vol. 96, KES2016 Elsevier, 2016.
Abstract | Links | BibTeX | Tags: Lexical Chains, Story Segmentation, Transcriptions, Video Retrieval, Word Embedding
@conference{chifu2016segchainw2v,
title = {Segchainw2v: Towards a generic automatic video segmentation framework, based on lexical chains of audio transcriptions and word embeddings},
author = {Adrian-Gabriel Chifu and Sébastien Fournier},
url = {https://reader.elsevier.com/reader/sd/pii/S1877050916319925?token=8EF351081CC26980139265A58715C3CD59C8D8708A523107D6B67DA85DBFBD02E9D644FBA6DEEA469D14B5B3E6D0BC24},
year = {2016},
date = {2016-09-01},
urldate = {2016-01-01},
booktitle = {20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems},
journal = {Procedia Computer Science},
volume = {96},
pages = {1371--1380},
publisher = {Elsevier},
series = {KES2016},
abstract = {With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChainW2V, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions and their vectorization. SegChainW2V takes into account the topic changes by perceiving the fiuctuations of the most frequent terms throughout the video, as well as their semantics through the word embedding vectorization.},
keywords = {Lexical Chains, Story Segmentation, Transcriptions, Video Retrieval, Word Embedding},
pubstate = {published},
tppubtype = {conference}
}
With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChainW2V, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions and their vectorization. SegChainW2V takes into account the topic changes by perceiving the fiuctuations of the most frequent terms throughout the video, as well as their semantics through the word embedding vectorization.
Adrian-Gabriel Chifu; Sébastien Fournier
Proceedings of the 6th international conference on web intelligence, mining and semantics, WIMS2016 2016.
Abstract | Links | BibTeX | Tags: Lexical Chains, Story Segmentation, Transcriptions, Video Retrieval
@conference{chifu2016segchain,
title = {SegChain: Towards a generic automatic video segmentation framework, based on lexical chains of audio transcriptions},
author = {Adrian-Gabriel Chifu and Sébastien Fournier},
url = {https://hal.archives-ouvertes.fr/hal-03018087/document},
year = {2016},
date = {2016-06-01},
urldate = {2016-01-01},
booktitle = {Proceedings of the 6th international conference on web intelligence, mining and semantics},
pages = {1--8},
series = {WIMS2016},
abstract = {With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChain, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions. SegChain takes into account the topic changes by perceiving the fluctuations of the most frequent terms throughout the video.},
keywords = {Lexical Chains, Story Segmentation, Transcriptions, Video Retrieval},
pubstate = {published},
tppubtype = {conference}
}
With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChain, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions. SegChain takes into account the topic changes by perceiving the fluctuations of the most frequent terms throughout the video.
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